{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for the bonus section.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1200: loss 89.622892\n",
      "iteration 100 / 1200: loss 88.026405\n",
      "iteration 200 / 1200: loss 86.434601\n",
      "iteration 300 / 1200: loss 84.912374\n",
      "iteration 400 / 1200: loss 83.420843\n",
      "iteration 500 / 1200: loss 81.937708\n",
      "iteration 600 / 1200: loss 80.509235\n",
      "iteration 700 / 1200: loss 79.098389\n",
      "iteration 800 / 1200: loss 77.700365\n",
      "iteration 900 / 1200: loss 76.334087\n",
      "iteration 1000 / 1200: loss 74.994287\n",
      "iteration 1100 / 1200: loss 73.699218\n",
      "iteration 0 / 1200: loss 758.569786\n",
      "iteration 100 / 1200: loss 622.628189\n",
      "iteration 200 / 1200: loss 511.352440\n",
      "iteration 300 / 1200: loss 420.251963\n",
      "iteration 400 / 1200: loss 345.660142\n",
      "iteration 500 / 1200: loss 284.612267\n",
      "iteration 600 / 1200: loss 234.622762\n",
      "iteration 700 / 1200: loss 193.706416\n",
      "iteration 800 / 1200: loss 160.213468\n",
      "iteration 900 / 1200: loss 132.793579\n",
      "iteration 1000 / 1200: loss 110.344487\n",
      "iteration 1100 / 1200: loss 91.963567\n",
      "iteration 0 / 1200: loss 7429.286420\n",
      "iteration 100 / 1200: loss 1003.166127\n",
      "iteration 200 / 1200: loss 142.198210\n",
      "iteration 300 / 1200: loss 26.845989\n",
      "iteration 400 / 1200: loss 11.390752\n",
      "iteration 500 / 1200: loss 9.320321\n",
      "iteration 600 / 1200: loss 9.042915\n",
      "iteration 700 / 1200: loss 9.005741\n",
      "iteration 800 / 1200: loss 9.000769\n",
      "iteration 900 / 1200: loss 9.000101\n",
      "iteration 1000 / 1200: loss 9.000010\n",
      "iteration 1100 / 1200: loss 8.999998\n",
      "iteration 0 / 1200: loss 85.083787\n",
      "iteration 100 / 1200: loss 71.258814\n",
      "iteration 200 / 1200: loss 59.977292\n",
      "iteration 300 / 1200: loss 50.736932\n",
      "iteration 400 / 1200: loss 43.159326\n",
      "iteration 500 / 1200: loss 36.982940\n",
      "iteration 600 / 1200: loss 31.905785\n",
      "iteration 700 / 1200: loss 27.743323\n",
      "iteration 800 / 1200: loss 24.347462\n",
      "iteration 900 / 1200: loss 21.568158\n",
      "iteration 1000 / 1200: loss 19.282738\n",
      "iteration 1100 / 1200: loss 17.424677\n",
      "iteration 0 / 1200: loss 803.655214\n",
      "iteration 100 / 1200: loss 115.465208\n",
      "iteration 200 / 1200: loss 23.264856\n",
      "iteration 300 / 1200: loss 10.910585\n",
      "iteration 400 / 1200: loss 9.256206\n",
      "iteration 500 / 1200: loss 9.034276\n",
      "iteration 600 / 1200: loss 9.004560\n",
      "iteration 700 / 1200: loss 9.000582\n",
      "iteration 800 / 1200: loss 9.000049\n",
      "iteration 900 / 1200: loss 8.999981\n",
      "iteration 1000 / 1200: loss 8.999967\n",
      "iteration 1100 / 1200: loss 8.999967\n",
      "iteration 0 / 1200: loss 7776.048255\n",
      "iteration 100 / 1200: loss 9.000002\n",
      "iteration 200 / 1200: loss 8.999997\n",
      "iteration 300 / 1200: loss 8.999997\n",
      "iteration 400 / 1200: loss 8.999996\n",
      "iteration 500 / 1200: loss 8.999996\n",
      "iteration 600 / 1200: loss 8.999997\n",
      "iteration 700 / 1200: loss 8.999997\n",
      "iteration 800 / 1200: loss 8.999996\n",
      "iteration 900 / 1200: loss 8.999996\n",
      "iteration 1000 / 1200: loss 8.999997\n",
      "iteration 1100 / 1200: loss 8.999997\n",
      "iteration 0 / 1200: loss 81.865055\n",
      "iteration 100 / 1200: loss 18.763034\n",
      "iteration 200 / 1200: loss 10.307688\n",
      "iteration 300 / 1200: loss 9.174582\n",
      "iteration 400 / 1200: loss 9.023056\n",
      "iteration 500 / 1200: loss 9.002768\n",
      "iteration 600 / 1200: loss 9.000077\n",
      "iteration 700 / 1200: loss 8.999776\n",
      "iteration 800 / 1200: loss 8.999685\n",
      "iteration 900 / 1200: loss 8.999690\n",
      "iteration 1000 / 1200: loss 8.999630\n",
      "iteration 1100 / 1200: loss 8.999653\n",
      "iteration 0 / 1200: loss 799.033142\n",
      "iteration 100 / 1200: loss 8.999963\n",
      "iteration 200 / 1200: loss 8.999968\n",
      "iteration 300 / 1200: loss 8.999963\n",
      "iteration 400 / 1200: loss 8.999961\n",
      "iteration 500 / 1200: loss 8.999959\n",
      "iteration 600 / 1200: loss 8.999961\n",
      "iteration 700 / 1200: loss 8.999960\n",
      "iteration 800 / 1200: loss 8.999972\n",
      "iteration 900 / 1200: loss 8.999964\n",
      "iteration 1000 / 1200: loss 8.999974\n",
      "iteration 1100 / 1200: loss 8.999959\n",
      "iteration 0 / 1200: loss 7477.185638\n",
      "iteration 100 / 1200: loss 8.999999\n",
      "iteration 200 / 1200: loss 8.999999\n",
      "iteration 300 / 1200: loss 9.000001\n",
      "iteration 400 / 1200: loss 9.000000\n",
      "iteration 500 / 1200: loss 9.000000\n",
      "iteration 600 / 1200: loss 8.999999\n",
      "iteration 700 / 1200: loss 9.000001\n",
      "iteration 800 / 1200: loss 9.000000\n",
      "iteration 900 / 1200: loss 8.999999\n",
      "iteration 1000 / 1200: loss 9.000000\n",
      "iteration 1100 / 1200: loss 9.000000\n",
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.096918 val accuracy: 0.093000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.108327 val accuracy: 0.098000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.408551 val accuracy: 0.411000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.105592 val accuracy: 0.118000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.416163 val accuracy: 0.417000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.400837 val accuracy: 0.420000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.413612 val accuracy: 0.417000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.408776 val accuracy: 0.394000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.305122 val accuracy: 0.279000\n",
      "best validation accuracy achieved during cross-validation: 0.420000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "for learning_rate in learning_rates:\n",
    "  for reg in regularization_strengths:\n",
    "    svm = LinearSVM()\n",
    "    svm.train(X_train_feats, y_train, learning_rate=learning_rate, reg=reg,\n",
    "              num_iters=1200, verbose=True)\n",
    "    y_train_pred = svm.predict(X_train_feats)\n",
    "    y_train_accuracy = np.mean(y_train == y_train_pred)\n",
    "    y_val_pred = svm.predict(X_val_feats)\n",
    "    y_val_accuracy = np.mean(y_val == y_val_pred)\n",
    "    results[(learning_rate, reg)] = (y_train_accuracy, y_val_accuracy)\n",
    "\n",
    "    if y_val_accuracy > best_val:\n",
    "      best_val = y_val_accuracy\n",
    "      best_svm = svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 5\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense? - Some of them look alike, though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 1.523354\n",
      "iteration 200 / 1000: loss 1.476359\n",
      "iteration 300 / 1000: loss 1.321006\n",
      "iteration 400 / 1000: loss 1.329968\n",
      "iteration 500 / 1000: loss 1.192251\n",
      "iteration 600 / 1000: loss 1.330266\n",
      "iteration 700 / 1000: loss 1.137551\n",
      "iteration 800 / 1000: loss 1.030730\n",
      "iteration 900 / 1000: loss 1.258514\n",
      "y_train_acc: 0.6296122448979592, y_val_acc: 0.574\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_net = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "learning_rates = [0.5]  # [1e-2, 1e-1, 0.5, 1]\n",
    "regularization_strengths = [5e-4]  # [2e-5, 5e-5, 8e-5, 1e-4, 5e-4, 1e-3, 3e-3]\n",
    "# hidden_sizes = [500]  # [200, 300, 400, 500, 600, 700]\n",
    "batch_sizes = [200]\n",
    "\n",
    "for learning_rate in learning_rates:\n",
    "  for reg in regularization_strengths:\n",
    "    for batch_size in batch_sizes:\n",
    "#       for hidden_size in hidden_sizes:\n",
    "      net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "      stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "                num_iters=1000, batch_size=batch_size,\n",
    "                learning_rate=learning_rate, learning_rate_decay=0.95,\n",
    "                reg=reg, verbose=True)\n",
    "      y_train_pred = net.predict(X_train_feats)\n",
    "      y_train_acc = np.mean(y_train == y_train_pred)\n",
    "      y_val_pred = net.predict(X_val_feats)\n",
    "      y_val_acc = np.mean(y_val == y_val_pred)\n",
    "      print('y_train_acc: %s, y_val_acc: %s' %(y_train_acc, y_val_acc))\n",
    "\n",
    "      if y_val_acc > best_val:\n",
    "        best_val = y_val_acc\n",
    "        best_net = net\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.582\n"
     ]
    }
   ],
   "source": [
    "# Run your neural net classifier on the test set. You should be able to\n",
    "# get more than 55% accuracy.\n",
    "\n",
    "test_acc = (net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus: Design your own features!\n",
    "\n",
    "You have seen that simple image features can improve classification performance. So far we have tried HOG and color histograms, but other types of features may be able to achieve even better classification performance.\n",
    "\n",
    "For bonus points, design and implement a new type of feature and use it for image classification on CIFAR-10. Explain how your feature works and why you expect it to be useful for image classification. Implement it in this notebook, cross-validate any hyperparameters, and compare its performance to the HOG + Color histogram baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus: Do something extra!\n",
    "Use the material and code we have presented in this assignment to do something interesting. Was there another question we should have asked? Did any cool ideas pop into your head as you were working on the assignment? This is your chance to show off!"
   ]
  }
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